Pick a PACC: Comparing domain-specific and general cognitive composites in Alzheimer disease research.

OBJECTIVE: We aimed to illustrate how complex cognitive data can be used to create domain-specific and general cognitive composites relevant to Alzheimer disease research. METHOD: Using equipercentile equating, we combined data from the Charles F. and Joanne Knight Alzheimer Disease Research Center that spanned multiple iterations of the Uniform Data Set. Exploratory factor analyses revealed four domain-specific composites representing episodic memory, semantic memory, working memory, and attention/processing speed. The previously defined preclinical Alzheimer disease cognitive composite (PACC) and a novel alternative, the Knight-PACC, were also computed alongside a global composite comprising all available tests. These three composites allowed us to compare the usefulness of domain and general composites in the context of predicting common Alzheimer disease biomarkers. RESULTS: General composites slightly outperformed domain-specific metrics in predicting imaging-derived amyloid, tau, and neurodegeneration burden. Power analyses revealed that the global, Knight-PACC, and attention and processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the Alzheimer Disease Cooperative Study-PACC required two to three times as many participants. CONCLUSIONS: Analyses of cognition with the Knight-PACC and our domain-specific composites offer researchers flexibility by providing validated outcome assessments that can equate across test versions to answer a wide range of questions regarding cognitive decline in normal aging and neurodegenerative disease. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease.

BACKGROUND: "Brain-predicted age" estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology. METHODS: We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-ß-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE Îµ4 carrier status, sex, and education. RESULTS: Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG. CONCLUSIONS: We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease.

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aß PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN).

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.

A practitioner's guide to geospatial analysis in a neuroimaging context.

Introduction: Health disparities arise from biological-environmental interactions. Neuroimaging cohorts are reaching sufficiently large sample sizes such that analyses could evaluate how the environment affects the brain. We present a practical guide for applying geospatial methods to a neuroimaging cohort. Methods: We estimated brain age gap (BAG) from structural magnetic resonance imaging (MRI) from 239 city-dwelling participants in St. Louis, Missouri. We compared these participants to population-level estimates from the American Community Survey (ACS). We used geospatial analysis to identify neighborhoods associated with patterns of altered brain structure. We also evaluated the relationship between Area Deprivation Index (ADI) and BAG. Results: We identify areas in St. Louis, Missouri that were significantly associated with higher BAG from a spatially representative cohort. We provide replication code. Conclusion: We observe a relationship between neighborhoods and brain health, which suggests that neighborhood-based interventions could be appropriate. We encourage other studies to geocode participant information to evaluate biological-environmental interaction.

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study.

Background: Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).

The brains of people with advanced Alzheimer's disease often look older than expected based on the patients' actual age. This 'brain age gap' (how old a brain appears compared to the person's chronological age) can be calculated thanks to machine learning algorithms which analyse images of the organ to detect changes related to aging. Traditionally, these models have relied on images of the brain structure, such as the size and thickness of various brain areas; more recent models have started to use activity data, such as how different brain regions work together to form functional networks. While the brain age gap is a useful measure for researchers who investigate aging and disease, it is not yet helpful for clinicians. For example, it is unclear whether the machine learning algorithm could detect changes in the brains of individuals in the initial stages of Alzheimer's disease, before they start to manifest cognitive symptoms. Millar et al. explored this question by testing whether models which incorporate structural and activity data could be more sensitive to these early changes. Three machine learning algorithms (relying on either structural data, activity data, or combination of both) were used to predict the brain ages of participants with no sign of disease; with biological markers of Alzheimer's disease but preserved cognitive functions; and with marked cognitive symptoms of the condition. Overall, the combined model was slightly better at predicting the brain age of healthy volunteers, and all three models indicated that patients with dementia had a brain which looked older than normal. For this group, the model based on structural data was also able to make predictions which reflected the severity of cognitive decline. Crucially, the algorithm which used activity data predicted that, in individuals with biological markers of Alzheimer's disease but no cognitive impairment, the brain looked in fact younger than chronological age. Exactly why this is the case remains unclear, but this signal may be driven by neural processes which unfold in the early stages of the disease. While more research is needed, the work by Millar et al. helps to explore how various types of machine learning models could one day be used to assess and predict brain health.

Wakeful Rest Benefits Recall, but Not Recognition, of Incidentally Encoded Memory Stimuli in Younger and Older Adults.

Older adults exhibit deficits in episodic memory tasks, which have often been attributed to encoding or retrieval deficits, with little attention to consolidation mechanisms. More recently, researchers have attempted to measure consolidation in the context of a behavioral experiment using the wakeful rest paradigm (i.e., a brief, quiet period of minimal stimulation, which facilitates memory performance, compared to a distractor task). Critically, older adults might not produce this effect, given established age differences in other episodic memory processes and mind-wandering. In three experiments, we directly compared younger and older adults in modified versions of the wakeful rest paradigm. Critically, we utilized incidental encoding procedures (all experiments) and abstract shape stimuli (in Experiment 3) to limit the possibility of retrieval practice or maintenance rehearsal as potential confounding mechanisms in producing the wakeful rest effect. Wakeful rest reliably and equally benefited recall of incidentally encoded words in both younger and older adults. In contrast, wakeful rest had no benefit for standard accuracy measures of recognition performance in verbal stimuli, although there was an effect in response latencies for non-verbal stimuli. Overall, these results suggest that the benefits of wakeful rest on episodic retrieval are preserved across age groups, and hence support age-independence in potential consolidation mechanisms as measured by wakeful rest. Further, these benefits do not appear to be dependent on the intentionality of encoding or variations in distractor task types. Finally, the lack of wakeful rest benefits on recognition performance might be driven by theoretical constraints on the effect or methodological limitations of recognition memory testing in the current paradigm.

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease.

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

Cultural differences in performance on Eriksen's flanker task.

Eriksen's zoom model of attention implies a trade-off between the breadth and resolution of representations of information. Following this perspective, we used Eriksen's flanker task to investigate culture's influence on attentional allocation and attentional resolution. In Experiment 1, the spatial distance of the flankers was varied to test whether people from Eastern cultures (here, Turks) experienced more interference than people from Western cultures (here, Americans) when flankers were further from the target. In Experiment 2, the contrast of the flankers was varied. The pattern of results shows that congruency of the flankers (Experiment 1) as well as the degree of contrast of the flankers compared with the target (Experiment 2) interact with participants' cultural background to differentially influence accuracy or reaction times. In addition, we used evidence accumulation modeling to jointly consider measures of speed and accuracy. Results indicate that to make decisions in the Eriksen flanker task, Turks both accumulate evidence faster and require more evidence than Americans do. These cultural differences in visual attention and decision-making have implications for a wide variety of cognitive processes.

Evaluating Cognitive Relationships with Resting-State and Task-driven Blood Oxygen Level-Dependent Variability.

Recent functional magnetic resonance imaging studies have reported that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is positively associated with task performance and, thus, may reflect a behaviorally sensitive signal. However, it is not clear whether estimates of resting-state and task-driven BOLD variability are differentially related to cognition, as they may be driven by distinct sources of variance in the BOLD signal. Moreover, other studies have suggested that age differences in resting-state BOLD variability may be particularly sensitive to individual differences in cardiovascular, rather than neural, factors. In this study, we tested relationships between measures of behavioral task performance and BOLD variability during both resting-state and task-driven runs of a Stroop and an animacy judgment task in a large, well-characterized sample of cognitively normal middle-aged to older adults. Resting-state BOLD variability was related to composite measures of global cognition and attentional control, but these relationships were eliminated after correction for age or cardiovascular estimates. In contrast, task-driven BOLD variability was related to attentional control measured both inside and outside the scanner, and importantly, these relationships persisted after correction for age and cardiovascular measures. Overall, these results suggest that BOLD variability is a behaviorally sensitive signal. However, resting-state and task-driven estimates of BOLD variability may differ in the degree to which they are sensitive to age-related, cardiovascular, and neural mechanisms.

Evaluating resting-state BOLD variability in relation to biomarkers of preclinical Alzheimer's disease.

Recent functional magnetic resonance imaging studies have demonstrated that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is related to age differences, cognition, and symptomatic Alzheimer's disease (AD). However, no studies have examined BOLD variability in the context of preclinical AD. We tested relationships between resting-state BOLD variability and biomarkers of amyloidosis, tauopathy, and neurodegeneration in a large (N = 321), well-characterized sample of cognitively normal adults (age = 39-93), using multivariate machine learning techniques. Furthermore, we controlled for cardiovascular health factors, which may contaminate resting-state BOLD variability estimates. BOLD variability, particularly in the default mode network, was related to cerebrospinal fluid (CSF) amyloid-ß42 but was not related to CSF phosphorylated tau-181. Furthermore, BOLD variability estimates were also related to markers of neurodegeneration, including CSF neurofilament light protein, hippocampal volume, and a cortical thickness composite. Notably, relationships with hippocampal volume and cortical thickness survived correction for cardiovascular health and also contributed to age-related differences in BOLD variability. Thus, BOLD variability may be sensitive to preclinical pathology, including amyloidosis and neurodegeneration in AD-sensitive areas.

Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach.

Recent functional magnetic resonance imaging (fMRI) studies report that moment-to-moment variability in the BOLD signal is related to differences in age and cognition and, thus, may be sensitive to age-dependent decline. However, head motion and/or cardiovascular health (CVH) may contaminate these relationships. We evaluated relationships between resting-state BOLD variability, age, and cognition, after characterizing and controlling for motion-related and cardiovascular influences, including pulse, blood pressure, BMI, and white matter hyperintensities (WMH), in a large (N = 422) resting-state fMRI sample of cognitively normal individuals (age 43-89). We found that resting-state BOLD variability was negatively related to age and positively related to cognition after maximally controlling for head motion. Age relationships also survived correction for CVH, but were greatly reduced when correcting for WMH alone. Our results suggest that network-based machine learning analyses of resting-state BOLD variability might yield reliable, sensitive measures to characterize age-related decline across a broad range of networks. Age-related differences in resting-state BOLD variability may be largely sensitive to processes related to WMH burden.

Multinomial models reveal deficits of two distinct controlled retrieval processes in aging and very mild Alzheimer disease.

Dual-process models of episodic retrieval reveal consistent deficits of controlled recollection in aging and Alzheimer disease (AD). In contrast, automatic familiarity is relatively spared. We extend standard dual-process models by showing the importance of a third capture process. Capture produces a failure to attempt recollection, which might reflect a distinct error from an inability to recollect when attempted (Jacoby et al. Journal of Experimental Psychology: General, 134(2), 131-148, 2005a). We used multinomial process tree (MPT) modeling to estimate controlled recollection and capture processes, as well as automatic retrieval processes, in a large group of middle-aged to older adults who were cognitively normal (N = 519) or diagnosed with the earliest detectable stage of AD (N = 107). Participants incidentally encoded word pairs (e.g., knee bone). At retrieval, participants completed cued word fragments (e.g., knee b_n_) with primes that were congruent (e.g., bone), incongruent (e.g., bend), or neutral (i.e., &&&) to the target (e.g., bone). MPT models estimated retrieval processes both at the group and the individual levels. A capture parameter was necessary to fit MPT models to the observed data, suggesting that dual-process models of this task can be contaminated by a capture process. In both group- and individual-level analyses, aging and very mild AD were associated with increased susceptibility to capture, decreased recollection, and no differences in automatic influences. These results suggest that it is important to consider two distinct modes of attentional control when modeling retrieval processes. Both forms of control (recollection and avoiding capture) are particularly sensitive to cognitive decline in aging and early-stage AD.

Process dissociation analyses of memory changes in healthy aging, preclinical, and very mild Alzheimer disease: Evidence for isolated recollection deficits.

OBJECTIVE: Recollection and familiarity are independent processes that contribute to memory performance. Recollection is dependent on attentional control, which has been shown to be disrupted in early stage Alzheimer's disease (AD), whereas familiarity is independent of attention. The present longitudinal study examines the sensitivity of recollection estimates based on Jacoby's (1991) process dissociation procedure to AD-related biomarkers in a large sample of well-characterized cognitively normal middle-aged and older adults (N = 519) and the extent to which recollection discriminates these individuals from individuals with very mild symptomatic AD (N = 64). METHOD: Participants studied word pairs (e.g., knee bone), then completed a primed, explicit, cued fragment-completion memory task (e.g., knee b_n_). Primes were either congruent with the correct response (e.g., bone), incongruent (e.g., bend), or neutral (e.g., &&&). This design allowed for the estimation of independent contributions of recollection and familiarity processes, using the process dissociation procedure. RESULTS: Recollection, but not familiarity, was impaired in healthy aging and in very mild AD. Recollection discriminated cognitively normal individuals from the earliest detectable stage of symptomatic AD above and beyond standard psychometric tests. In cognitively normal individuals, baseline CSF measures indicative of AD pathology were related to lower initial recollection and less practice-related improvement in recollection over time. Finally, presence of amyloid plaques, as imaged by PIB-PET, was also related to less improvement in recollection over time. CONCLUSIONS: These findings suggest that attention-demanding memory processes, such as recollection, may be particularly sensitive to both symptomatic and preclinical AD pathology. (PsycINFO Database Record

Reducing the framing effect in older and younger adults by encouraging analytic processing.

OBJECTIVES: The present study explored whether the framing effect could be reduced in older and younger adults using techniques that influenced the accessibility of information relevant to the decision-making processing. Accessibility was manipulated indirectly in Experiment 1 by having participants engage in concurrent tasks, and directly in Experiment 2, through an instructions manipulation that required participants to maintain a goal of analytic processing throughout the experimental trial. METHODS: We tested 120 older and 120 younger adults in Experiment 1. Participants completed 28 decision trials while concurrently either performing a probability calculation task or a memory task. In Experiment 2, we tested 136 older and 136 younger adults. Participants completed 48 decision trials after either having been instructed to "think like a scientist" or base decisions on "gut reactions." RESULTS: Results demonstrated that the framing effect was reduced in older and younger adults in the probability calculation task in Experiment 1 and under the "think like a scientist" instructions manipulation in Experiment 2. DISCUSSION: These results suggest that when information relevant to unbiased decision making was made more accessible, both older and younger adults were able to reduce susceptibility to the framing effect.